José A. R. Fonollosa

Also published as: Jose A. R. Fonollosa, Jose A. R. Fonollosa, José A. R. Fonollosa, José A.R. Fonollosa


2021

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Multilingual Machine Translation: Closing the Gap between Shared and Language-specific Encoder-Decoders
Carlos Escolano | Marta R. Costa-jussà | José A. R. Fonollosa | Mikel Artetxe
Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume

State-of-the-art multilingual machine translation relies on a universal encoder-decoder, which requires retraining the entire system to add new languages. In this paper, we propose an alternative approach that is based on language-specific encoder-decoders, and can thus be more easily extended to new languages by learning their corresponding modules. So as to encourage a common interlingua representation, we simultaneously train the N initial languages. Our experiments show that the proposed approach outperforms the universal encoder-decoder by 3.28 BLEU points on average, while allowing to add new languages without the need to retrain the rest of the modules. All in all, our work closes the gap between shared and language-specific encoderdecoders, advancing toward modular multilingual machine translation systems that can be flexibly extended in lifelong learning settings.

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End-to-End Speech Translation with Pre-trained Models and Adapters: UPC at IWSLT 2021
Gerard I. Gállego | Ioannis Tsiamas | Carlos Escolano | José A. R. Fonollosa | Marta R. Costa-jussà
Proceedings of the 18th International Conference on Spoken Language Translation (IWSLT 2021)

This paper describes the submission to the IWSLT 2021 offline speech translation task by the UPC Machine Translation group. The task consists of building a system capable of translating English audio recordings extracted from TED talks into German text. Submitted systems can be either cascade or end-to-end and use a custom or given segmentation. Our submission is an end-to-end speech translation system, which combines pre-trained models (Wav2Vec 2.0 and mBART) with coupling modules between the encoder and decoder, and uses an efficient fine-tuning technique, which trains only 20% of its total parameters. We show that adding an Adapter to the system and pre-training it, can increase the convergence speed and the final result, with which we achieve a BLEU score of 27.3 on the MuST-C test set. Our final model is an ensemble that obtains 28.22 BLEU score on the same set. Our submission also uses a custom segmentation algorithm that employs pre-trained Wav2Vec 2.0 for identifying periods of untranscribable text and can bring improvements of 2.5 to 3 BLEU score on the IWSLT 2019 test set, as compared to the result with the given segmentation.

2020

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Enhancing Sequence-to-Sequence Modelling for RDF triples to Natural Text
Oriol Domingo | David Bergés | Roser Cantenys | Roger Creus | José A. R. Fonollosa
Proceedings of the 3rd International Workshop on Natural Language Generation from the Semantic Web (WebNLG+)

establishes key guidelines on how, which and when Machine Translation (MT) techniques are worth applying to RDF-to-Text task. Not only do we apply and compare the most prominent MT architecture, the Transformer, but we also analyze state-of-the-art techniques such as Byte Pair Encoding or Back Translation to demonstrate an improvement in generalization. In addition, we empirically show how to tailor these techniques to enhance models relying on learned embeddings rather than using pretrained ones. Automatic metrics suggest that Back Translation can significantly improve model performance up to 7 BLEU points, hence, opening a window for surpassing state-of-the-art results with appropriate architectures.

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The UPC RDF-to-Text System at WebNLG Challenge 2020
David Bergés | Roser Cantenys | Roger Creus | Oriol Domingo | José A. R. Fonollosa
Proceedings of the 3rd International Workshop on Natural Language Generation from the Semantic Web (WebNLG+)

This work describes the end-to-end system architecture presented at WebNLG Challenge 2020. The system follows the traditional Machine Translation (MT) pipeline, based on the Transformer model, applied in most text-totext problems. Our solution is enriched by means of a Back Translation step over the original corpus. Thus, the system directly relies on lexicalise format since the synthetic data limits the use of delexicalisation.

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Syntax-driven Iterative Expansion Language Models for Controllable Text Generation
Noe Casas | José A. R. Fonollosa | Marta R. Costa-jussà
Proceedings of the Fourth Workshop on Structured Prediction for NLP

The dominant language modeling paradigm handles text as a sequence of discrete tokens. While that approach can capture the latent structure of the text, it is inherently constrained to sequential dynamics for text generation. We propose a new paradigm for introducing a syntactic inductive bias into neural text generation, where the dependency parse tree is used to drive the Transformer model to generate sentences iteratively. Our experiments show that this paradigm is effective at text generation, with quality between LSTMs and Transformers, and comparable diversity, requiring less than half their decoding steps, and its generation process allows direct control over the syntactic constructions of the generated text, enabling the induction of stylistic variations.

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Combining Subword Representations into Word-level Representations in the Transformer Architecture
Noe Casas | Marta R. Costa-jussà | José A. R. Fonollosa
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop

In Neural Machine Translation, using word-level tokens leads to degradation in translation quality. The dominant approaches use subword-level tokens, but this increases the length of the sequences and makes it difficult to profit from word-level information such as POS tags or semantic dependencies. We propose a modification to the Transformer model to combine subword-level representations into word-level ones in the first layers of the encoder, reducing the effective length of the sequences in the following layers and providing a natural point to incorporate extra word-level information. Our experiments show that this approach maintains the translation quality with respect to the normal Transformer model when no extra word-level information is injected and that it is superior to the currently dominant method for incorporating word-level source language information to models based on subword-level vocabularies.

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The TALP-UPC System Description for WMT20 News Translation Task: Multilingual Adaptation for Low Resource MT
Carlos Escolano | Marta R. Costa-jussà | José A. R. Fonollosa
Proceedings of the Fifth Conference on Machine Translation

In this article, we describe the TALP-UPC participation in the WMT20 news translation shared task for Tamil-English. Given the low amount of parallel training data, we resort to adapt the task to a multilingual system to benefit from the positive transfer from high resource languages. We use iterative backtranslation to fine-tune the system and benefit from the monolingual data available. In order to measure the effectivity of such methods, we compare our results to a bilingual baseline system.

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Towards Mitigating Gender Bias in a decoder-based Neural Machine Translation model by Adding Contextual Information
Christine Basta | Marta R. Costa-jussà | José A. R. Fonollosa
Proceedings of the The Fourth Widening Natural Language Processing Workshop

Gender bias negatively impacts many natural language processing applications, including machine translation (MT). The motivation behind this work is to study whether recent proposed MT techniques are significantly contributing to attenuate biases in document-level and gender-balanced data. For the study, we consider approaches of adding the previous sentence and the speaker information, implemented in a decoder-based neural MT system. We show improvements both in translation quality (+1 BLEU point) as well as in gender bias mitigation on WinoMT (+5% accuracy).

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Automatic Spanish Translation of SQuAD Dataset for Multi-lingual Question Answering
Casimiro Pio Carrino | Marta R. Costa-jussà | José A. R. Fonollosa
Proceedings of the 12th Language Resources and Evaluation Conference

Recently, multilingual question answering became a crucial research topic, and it is receiving increased interest in the NLP community. However, the unavailability of large-scale datasets makes it challenging to train multilingual QA systems with performance comparable to the English ones. In this work, we develop the Translate Align Retrieve (TAR) method to automatically translate the Stanford Question Answering Dataset (SQuAD) v1.1 to Spanish. We then used this dataset to train Spanish QA systems by fine-tuning a Multilingual-BERT model. Finally, we evaluated our QA models with the recently proposed MLQA and XQuAD benchmarks for cross-lingual Extractive QA. Experimental results show that our models outperform the previous Multilingual-BERT baselines achieving the new state-of-the-art values of 68.1 F1 on the Spanish MLQA corpus and 77.6 F1 on the Spanish XQuAD corpus. The resulting, synthetically generated SQuAD-es v1.1 corpora, with almost 100% of data contained in the original English version, to the best of our knowledge, is the first large-scale QA training resource for Spanish.

2019

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From Bilingual to Multilingual Neural Machine Translation by Incremental Training
Carlos Escolano | Marta R. Costa-jussà | José A. R. Fonollosa
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop

Multilingual Neural Machine Translation approaches are based on the use of task specific models and the addition of one more language can only be done by retraining the whole system. In this work, we propose a new training schedule that allows the system to scale to more languages without modification of the previous components based on joint training and language-independent encoder/decoder modules allowing for zero-shot translation. This work in progress shows close results to state-of-the-art in the WMT task.

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BERT Masked Language Modeling for Co-reference Resolution
Felipe Alfaro | Marta R. Costa-jussà | José A. R. Fonollosa
Proceedings of the First Workshop on Gender Bias in Natural Language Processing

This paper explains the TALP-UPC participation for the Gendered Pronoun Resolution shared-task of the 1st ACL Workshop on Gender Bias for Natural Language Processing. We have implemented two models for mask language modeling using pre-trained BERT adjusted to work for a classification problem. The proposed solutions are based on the word probabilities of the original BERT model, but using common English names to replace the original test names.

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The TALP-UPC Machine Translation Systems for WMT19 News Translation Task: Pivoting Techniques for Low Resource MT
Noe Casas | José A. R. Fonollosa | Carlos Escolano | Christine Basta | Marta R. Costa-jussà
Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1)

In this article, we describe the TALP-UPC research group participation in the WMT19 news translation shared task for Kazakh-English. Given the low amount of parallel training data, we resort to using Russian as pivot language, training subword-based statistical translation systems for Russian-Kazakh and Russian-English that were then used to create two synthetic pseudo-parallel corpora for Kazakh-English and English-Kazakh respectively. Finally, a self-attention model based on the decoder part of the Transformer architecture was trained on the two pseudo-parallel corpora.

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Terminology-Aware Segmentation and Domain Feature for the WMT19 Biomedical Translation Task
Casimiro Pio Carrino | Bardia Rafieian | Marta R. Costa-jussà | José A. R. Fonollosa
Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2)

In this work, we give a description of the TALP-UPC systems submitted for the WMT19 Biomedical Translation Task. Our proposed strategy is NMT model-independent and relies only on one ingredient, a biomedical terminology list. We first extracted such a terminology list by labelling biomedical words in our training dataset using the BabelNet API. Then, we designed a data preparation strategy to insert the terms information at a token level. Finally, we trained the Transformer model with this terms-informed data. Our best-submitted system ranked 2nd and 3rd for Spanish-English and English-Spanish translation directions, respectively.

2018

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The TALP-UPC Machine Translation Systems for WMT18 News Shared Translation Task
Noe Casas | Carlos Escolano | Marta R. Costa-jussà | José A. R. Fonollosa
Proceedings of the Third Conference on Machine Translation: Shared Task Papers

In this article we describe the TALP-UPC research group participation in the WMT18 news shared translation task for Finnish-English and Estonian-English within the multi-lingual subtrack. All of our primary submissions implement an attention-based Neural Machine Translation architecture. Given that Finnish and Estonian belong to the same language family and are similar, we use as training data the combination of the datasets of both language pairs to paliate the data scarceness of each individual pair. We also report the translation quality of systems trained on individual language pair data to serve as baseline and comparison reference.

2017

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Byte-based Neural Machine Translation
Marta R. Costa-jussà | Carlos Escolano | José A. R. Fonollosa
Proceedings of the First Workshop on Subword and Character Level Models in NLP

This paper presents experiments comparing character-based and byte-based neural machine translation systems. The main motivation of the byte-based neural machine translation system is to build multi-lingual neural machine translation systems that can share the same vocabulary. We compare the performance of both systems in several language pairs and we see that the performance in test is similar for most language pairs while the training time is slightly reduced in the case of byte-based neural machine translation.

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The TALP-UPC Neural Machine Translation System for German/Finnish-English Using the Inverse Direction Model in Rescoring
Carlos Escolano | Marta R. Costa-jussà | José A. R. Fonollosa
Proceedings of the Second Conference on Machine Translation

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Character-level Intra Attention Network for Natural Language Inference
Han Yang | Marta R. Costa-jussà | José A. R. Fonollosa
Proceedings of the 2nd Workshop on Evaluating Vector Space Representations for NLP

Natural language inference (NLI) is a central problem in language understanding. End-to-end artificial neural networks have reached state-of-the-art performance in NLI field recently. In this paper, we propose Character-level Intra Attention Network (CIAN) for the NLI task. In our model, we use the character-level convolutional network to replace the standard word embedding layer, and we use the intra attention to capture the intra-sentence semantics. The proposed CIAN model provides improved results based on a newly published MNLI corpus.

2016

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The TALPUPC Spanish–English WMT Biomedical Task: Bilingual Embeddings and Char-based Neural Language Model Rescoring in a Phrase-based System
Marta R. Costa-jussà | Cristina España-Bonet | Pranava Madhyastha | Carlos Escolano | José A. R. Fonollosa
Proceedings of the First Conference on Machine Translation: Volume 2, Shared Task Papers

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Character-based Neural Machine Translation
Marta R. Costa-jussà | José A. R. Fonollosa
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

2013

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The TALP-UPC Phrase-Based Translation Systems for WMT13: System Combination with Morphology Generation, Domain Adaptation and Corpus Filtering
Lluís Formiga | Marta R. Costa-jussà | José B. Mariño | José A. R. Fonollosa | Alberto Barrón-Cedeño | Lluís Màrquez
Proceedings of the Eighth Workshop on Statistical Machine Translation

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The TALP-UPC Approach to System Selection: Asiya Features and Pairwise Classification Using Random Forests
Lluís Formiga | Meritxell Gonzàlez | Alberto Barrón-Cedeño | José A. R. Fonollosa | Lluís Màrquez
Proceedings of the Eighth Workshop on Statistical Machine Translation

2012

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BUCEADOR, a multi-language search engine for digital libraries
Jordi Adell | Antonio Bonafonte | Antonio Cardenal | Marta R. Costa-Jussà | José A. R. Fonollosa | Asunción Moreno | Eva Navas | Eduardo R. Banga
Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC'12)

This paper presents a web-based multimedia search engine built within the Buceador (www.buceador.org) research project. A proof-of-concept tool has been implemented which is able to retrieve information from a digital library made of multimedia documents in the 4 official languages in Spain (Spanish, Basque, Catalan and Galician). The retrieved documents are presented in the user language after translation and dubbing (the four previous languages + English). The paper presents the tool functionality, the architecture, the digital library and provide some information about the technology involved in the fields of automatic speech recognition, statistical machine translation, text-to-speech synthesis and information retrieval. Each technology has been adapted to the purposes of the presented tool as well as to interact with the rest of the technologies involved.

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The TALP-UPC phrase-based translation systems for WMT12: Morphology simplification and domain adaptation
Lluís Formiga | Carlos A. Henríquez Q. | Adolfo Hernández | José B. Mariño | Enric Monte | José A. R. Fonollosa
Proceedings of the Seventh Workshop on Statistical Machine Translation

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Dealing with Input Noise in Statistical Machine Translation
Lluis Formiga | Jose A. R. Fonollosa
Proceedings of COLING 2012: Posters

2010

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Linguistic-based Evaluation Criteria to identify Statistical Machine Translation Errors
Mireia Farrús | Marta R. Costa-jussà | José B. Mariño | José A. R. Fonollosa
Proceedings of the 14th Annual conference of the European Association for Machine Translation

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Using Linear Interpolation and Weighted Reordering Hypotheses in the Moses System
Marta R. Costa-jussà | José A. R. Fonollosa
Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10)

This paper proposes to introduce a novel reordering model in the open-source Moses toolkit. The main idea is to provide weighted reordering hypotheses to the SMT decoder. These hypotheses are built using a first-step Ngram-based SMT translation from a source language into a third representation that is called reordered source language. Each hypothesis has its own weight provided by the Ngram-based decoder. This proposed reordering technique offers a better and more efficient translation when compared to both the distance-based and the lexicalized reordering. In addition to this reordering approach, this paper describes a domain adaptation technique which is based on a linear combination of an specific in-domain and an extra out-domain translation models. Results for both approaches are reported in the Arabic-to-English 2008 IWSLT task. When implementing the weighted reordering hypotheses and the domain adaptation technique in the final translation system, translation results reach improvements up to 2.5 BLEU compared to a standard state-of-the-art Moses baseline system.

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Automatic and Human Evaluation Study of a Rule-based and a Statistical Catalan-Spanish Machine Translation Systems
Marta R. Costa-jussà | Mireia Farrús | José B. Mariño | José A. R. Fonollosa
Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10)

Machine translation systems can be classified into rule-based and corpus-based approaches, in terms of their core technology. Since both paradigms have largely been used during the last years, one of the aims in the research community is to know how these systems differ in terms of translation quality. To this end, this paper reports a study and comparison of a rule-based and a corpus-based (particularly, statistical) Catalan-Spanish machine translation systems, both of them freely available in the web. The translation quality analysis is performed under two different domains: journalistic and medical. The systems are evaluated by using standard automatic measures, as well as by native human evaluators. Automatic results show that the statistical system performs better than the rule-based system. Human judgements show that in the Spanish-to-Catalan direction the statistical system also performs better than the rule-based system, while in the Catalan-to-Spanish direction is the other way round. Although the statistical system obtains the best automatic scores, its errors tend to be more penalized by human judgements than the errors of the rule-based system. This can be explained because statistical errors are usually unexpected and they do not follow any pattern.

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Towards Improving English-Latvian Translation: A System Comparison and a New Rescoring Feature
Maxim Khalilov | José A. R. Fonollosa | Inguna Skadin̨a | Edgars Brālītis | Lauma Pretkalnin̨a
Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10)

Translation into the languages with relatively free word order has received a lot less attention than translation into fixed word order languages (English), or into analytical languages (Chinese). At the same time this translation task is found among the most difficult challenges for machine translation (MT), and intuitively it seems that there is some space in improvement intending to reflect the free word order structure of the target language. This paper presents a comparative study of two alternative approaches to statistical machine translation (SMT) and their application to a task of English-to-Latvian translation. Furthermore, a novel feature intending to reflect the relatively free word order scheme of the Latvian language is proposed and successfully applied on the n-best list rescoring step. Moving beyond classical automatic scores of translation quality that are classically presented in MT research papers, we contribute presenting a manual error analysis of MT systems output that helps to shed light on advantages and disadvantages of the SMT systems under consideration.

2009

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N-Gram-Based Statistical Machine Translation versus Syntax Augmented Machine Translation: Comparison and System Combination
Maxim Khalilov | José A. R. Fonollosa
Proceedings of the 12th Conference of the European Chapter of the ACL (EACL 2009)

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A New Subtree-Transfer Approach to Syntax-Based Reordering for Statistical Machine Translation
Maxim Khalilov | José A. R. Fonollosa | Mark Dras
Proceedings of the 13th Annual conference of the European Association for Machine Translation

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The TALP-UPC Phrase-Based Translation System for EACL-WMT 2009
José A. R. Fonollosa | Maxim Khalilov | Marta R. Costa-jussà | José B. Mariño | Carlos A. Henráquez Q. | Adolfo Hernández H. | Rafael E. Banchs
Proceedings of the Fourth Workshop on Statistical Machine Translation

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Coupling Hierarchical Word Reordering and Decoding in Phrase-Based Statistical Machine Translation
Maxim Khalilov | José A. R. Fonollosa | Mark Dras
Proceedings of the Third Workshop on Syntax and Structure in Statistical Translation (SSST-3) at NAACL HLT 2009

2008

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Using Reordering in Statistical Machine Translation based on Alignment Block Classification
Marta R. Costa-jussà | José A. R. Fonollosa | Enric Monte
Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC'08)

Statistical Machine Translation (SMT) is based on alignment models which learn from bilingual corpora the word correspondences between source and target language. These models are assumed to be capable of learning reorderings of sequences of words. However, the difference in word order between two languages is one of the most important sources of errors in SMT. This paper proposes a Recursive Alignment Block Classification algorithm (RABCA) that can take advantage of inductive learning in order to solve reordering problems. This algorithm should be able to cope with swapping examples seen during training; it should infer properties that might permit to reorder pairs of blocks (sequences of words) which did not appear during training; and finally it should be robust with respect to training errors and ambiguities. Experiments are reported on the EuroParl task and RABCA is tested using two state-of-the-art SMT systems: a phrased-based and an Ngram-based. In both cases, RABCA improves results.

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The TALP&I2R SMT systems for IWSLT 2008.
Maxim Khalilov | Maria R. Costa-jussà | Carlos A. Henríquez Q. | José A. R. Fonollosa | Adolfo Hernández H. | José B. Mariño | Rafael E. Banchs | Chen Boxing | Min Zhang | Aiti Aw | Haizhou Li
Proceedings of the 5th International Workshop on Spoken Language Translation: Evaluation Campaign

This paper gives a description of the statistical machine translation (SMT) systems developed at the TALP Research Center of the UPC (Universitat Polite`cnica de Catalunya) for our participation in the IWSLT’08 evaluation campaign. We present Ngram-based (TALPtuples) and phrase-based (TALPphrases) SMT systems. The paper explains the 2008 systems’ architecture and outlines translation schemes we have used, mainly focusing on the new techniques that are challenged to improve speech-to-speech translation quality. The novelties we have introduced are: improved reordering method, linear combination of translation and reordering models and new technique dealing with punctuation marks insertion for a phrase-based SMT system. This year we focus on the Arabic-English, Chinese-Spanish and pivot Chinese-(English)-Spanish translation tasks.

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Computing multiple weighted reordering hypotheses for a phrase-based statistical machine translation system
Marta R. Costa-Jussà | José A. R. Fonollosa
Proceedings of the 8th Conference of the Association for Machine Translation in the Americas: Research Papers

Reordering is one source of error in statistical machine translation (SMT). This paper extends the study of the statistical machine reordering (SMR) approach, which uses the powerful techniques of the SMT systems to solve reordering problems. Here, the novelties yield in: (1) using the SMR approach in a SMT phrase-based system, (2) adding a feature function in the SMR step, and (3) analyzing the reordering hypotheses at several stages. Coherent improvements are reported in the TC-STAR task (Es/En) at a relatively low computational cost.

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The TALP-UPC Ngram-Based Statistical Machine Translation System for ACL-WMT 2008
Maxim Khalilov | Adolfo Hernández H. | Marta R. Costa-jussà | Josep M. Crego | Carlos A. Henríquez Q. | Patrik Lambert | José A. R. Fonollosa | José B. Mariño | Rafael E. Banchs
Proceedings of the Third Workshop on Statistical Machine Translation

2007

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Smooth Bilingual N-Gram Translation
Holger Schwenk | Marta R. Costa-jussà | Jose A. R. Fonollosa
Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL)

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Ngram-Based Statistical Machine Translation Enhanced with Multiple Weighted Reordering Hypotheses
Marta R. Costa-jussà | Josep M. Crego | Patrik Lambert | Maxim Khalilov | José A. R. Fonollosa | José B. Mariño | Rafael E. Banchs
Proceedings of the Second Workshop on Statistical Machine Translation

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Analysis of Statistical and Morphological Classes to Generate Weigthed Reordering Hypotheses on a Statistical Machine Translation System
Marta R. Costa-jussà | José A. R. Fonollosa
Proceedings of the Second Workshop on Statistical Machine Translation

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The TALP ngram-based SMT system for IWSLT 2007
Patrik Lambert | Marta R. Costa-jussà | Josep M. Crego | Maxim Khalilov | José B. Mariño | Rafael E. Banchs | José A. R. Fonollosa | Holger Schwenk
Proceedings of the Fourth International Workshop on Spoken Language Translation

This paper describes TALPtuples, the 2007 N-gram-based statistical machine translation system developed at the TALP Research Center of the UPC (Universitat Polite`cnica de Catalunya) in Barcelona. Emphasis is put on improvements and extensions of the system of previous years. Mainly, these include optimizing alignment parameters in function of translation metric scores and rescoring with a neural network language model. Results on two translation directions are reported, namely from Arabic and Chinese into English, thoroughly explaining all language-related preprocessing and translation schemes.

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Analysis and System Combination of Phrase- and N-Gram-Based Statistical Machine Translation Systems
Marta R. Costa-jussà | Josep M. Crego | David Vilar | José A. R. Fonollosa | José B. Mariño | Hermann Ney
Human Language Technologies 2007: The Conference of the North American Chapter of the Association for Computational Linguistics; Companion Volume, Short Papers

2006

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N-gram-based Machine Translation
José Mariño | Rafael E. Banchs | Josep M. Crego | Adrià de Gispert | Patrik Lambert | José A. R. Fonollosa | Marta R. Costa-jussà
Computational Linguistics, Volume 32, Number 4, December 2006

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The TALP Ngram-based SMT systems for IWSLT 2006
Josep M. Crego | Adrià de Gispert | Patrick Lambert | Maxim Khalilov | Marta R. Costa-jussà | José B. Mariño | Rafael Banchs | José A. R. Fonollosa
Proceedings of the Third International Workshop on Spoken Language Translation: Evaluation Campaign

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TALP phrase-based system and TALP system combination for IWSLT 2006
Marta R. Costa-jussà | Josep M. Crego | Adrià de Gispert | Patrik Lambert | Maxim Khalilov | José A. R. Fonollosa | José B. Mariño | Rafael Banchs
Proceedings of the Third International Workshop on Spoken Language Translation: Evaluation Campaign

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Continuous space language models for the IWSLT 2006 task
Holger Schwenk | Marta R. Costa-jussà | José A. R. Fonollosa
Proceedings of the Third International Workshop on Spoken Language Translation: Papers

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Statistical Machine Reordering
Marta R. Costa-jussà | José A. R. Fonollosa
Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing

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TALP Phrase-based statistical translation system for European language pairs
Marta R. Costa-jussà | Josep M. Crego | Adrià de Gispert | Patrik Lambert | Maxim Khalilov | José B. Mariño | José A. R. Fonollosa | Rafael Banchs
Proceedings on the Workshop on Statistical Machine Translation

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N-gram-based SMT System Enhanced with Reordering Patterns
Josep M. Crego | Adrià de Gispert | Patrik Lambert | Marta R. Costa-jussà | Maxim Khalilov | Rafael Banchs | José B. Mariño | José A. R. Fonollosa
Proceedings on the Workshop on Statistical Machine Translation

2005

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Bilingual N-gram Statistical Machine Translation
José B. Mariño | Rafael E. Banchs | Josep M. Crego | Adrià de Gispert | Patrik Lambert | José A. R. Fonollosa | Marta Ruiz
Proceedings of Machine Translation Summit X: Papers

This paper describes a statistical machine translation system that uses a translation model which is based on bilingual n-grams. When this translation model is log-linearly combined with four specific feature functions, state of the art translations are achieved for Spanish-to-English and English-to-Spanish translation tasks. Some specific results obtained for the EPPS (European Parliament Plenary Sessions) data are presented and discussed. Finally, future research issues are depicted.

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Ngram-based versus Phrase-based Statistical Machine Translation
Josep M. Crego | Marta R. Costa-Jussa | Jose B. Marino | Jose A. R. Fonollosa
Proceedings of the Second International Workshop on Spoken Language Translation

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Tuning a phrase-based statistical translation system for the IWSLT 2005 Chinese to English and Arabic to English tasks
Marta R. Costa-Jussa | Jose A. R. Fonollosa
Proceedings of the Second International Workshop on Spoken Language Translation

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Improving Phrase-Based Statistical Translation by Modifying Phrase Extraction and Including Several Features
Marta Ruiz Costa-jussà | José A. R. Fonollosa
Proceedings of the ACL Workshop on Building and Using Parallel Texts

2000

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SpeechDat-Car Fixed Platform
José A.R. Fonollosa | Asunción Moreno
Proceedings of the Second International Conference on Language Resources and Evaluation (LREC’00)